Bayesian inference

Improved Bayesian inference of hybrids using genome sequences

The paper develops a Bayesian method for inferring hybrids and back-crosses from genome sequences, showing improved handling of population haplotype uncertainty and comparable performance to existing methods.

Diminished Stimulus-Evoked Activity During Sustained Attention Without Behavioural Cost in ADHD

This study finds no group differences in sustained attention performance or wake slow wave activity between ADHD and neurotypical adults, but identifies neural differences suggesting compensatory mechanisms in ADHD.

Charge-trap flash memory cells of the brain

The paper proposes quantum tunneling in myelin layers as a mechanism for memory storage, suggesting ionic charges represent binary data in the brain.

Characterizing the impact of plasma protein levels on human brain structure and disorders leveraging integrative multi-omics analysis

This study identifies plasma proteins linked to brain structure and neurological disorders using multi-omics integration and Mendelian randomization.

A Bayesian Network-Based Framework for Causal Cancer Drug Target Discovery Integrating Patient and Cell Line Data

BayesTx identifies causal cancer drug targets by integrating patient and cell line data using Bayesian networks, ranking 47 transcription factors and validating them with survival and drug response data.

BraiNN: A Modern Simulator for Clinically Feasible Personalized Whole-Brain Network Modeling

BraiNN is a framework for fast, personalized whole-brain network modeling using neural mass models, achieving rapid parameter fitting on consumer GPUs for clinical applications.

Advancing in silico drug design with Bayesian refinement of AlphaFold models

This paper introduces bAIes, a method that improves AlphaFold2 models using Bayesian inference to enhance virtual screening accuracy for drug discovery.

Uncertainty-aware quantitative analysis of high-throughput live cell migration data

The paper introduces cellmig, a Bayesian tool for quantifying cell migration velocity with uncertainty, improving reproducibility and biological insight in high-throughput assays.

Shared Genetic Architecture and Neurobiological Pathways of Problematic Alcohol Use and Anxiety Disorders

This study identifies shared genetic and neurobiological pathways between problematic alcohol use and anxiety disorders using genomic analyses.

Overcoming the Limits of Traditional Rate Calculations from Sparse Concentration Data: A Probabilistic Framework for Bioprocess Modeling

This paper introduces a probabilistic framework using Bayesian inference to improve metabolic rate estimation from sparse concentration data in bioprocesses.

Estimating Muscle Parameters via Hierarchical Bayesian Inference

This paper introduces a hierarchical Bayesian framework to estimate subject-specific muscle parameters using EMG and torque data, achieving high accuracy and robustness in musculoskeletal modeling.

Ancient Rapid Radiation Underlies Persistent Phylogenomic Conflict in Early Collembola Diversification

This study resolves phylogenetic conflicts in Collembola by showing ancient rapid radiation and incomplete lineage sorting, supporting Poduromorpha as the earliest-diverging lineage.

A deep-learning-informed prior and Bayesian model for differential AP-MS interactome analysis

BayesInteractomics improves AP-MS interactome analysis by combining deep learning and Bayesian statistics to better distinguish true interactions from background noise.

Vibration's frequency and intensity for optimal setup for enhancement bone response in small rodents: A systematic review and Bayesian network meta-analysis

This study identifies optimal vibration parameters for enhancing trabecular bone volume in small rodents through systematic review and Bayesian network meta-analysis.

A meta-analysis resolves the huntingtin interactome into coactivator losses and a robust proteostatic and synaptic gain network

This paper integrates HTT affinity-proteomics datasets to show polyQ expansion causes loss of transcription coactivators and gain of proteostatic and synaptic contacts.

Model-optimized stimulus distortions for adaptive estimation of individual sensory representations

The paper introduces a Bayesian adaptive method to estimate individual sensory representations by optimizing stimulus distortions based on perceptual judgments.

TrustPGS: When can a polygenic score be trusted? A per-individual reliability framework across ancestries

TrustPGS evaluates per-individual reliability of polygenic scores across ancestries by analyzing distributional patterns in diverse populations.

Recalibrating Mendelian randomization under winner's curse, sample structure and polygenicity

This paper introduces a multivariable Rao-Blackwellization method to correct Mendelian randomization biases from winner's curse, polygenicity, and sample structure, improving type-I error control and inference robustness.

CFD-based Bayesian Optimization of Stirring Strategies in Stirred Tank Cultures of Pluripotent Stem Cell Spheroids

This study optimizes stirring strategies for pluripotent stem cell spheroids using CFD and Bayesian optimization, improving suspension stability and reducing mechanical stress.

msBayesImpute: A Versatile Framework for Addressing Missing Values in Biomedical Mass Spectrometry Proteomics Data

msBayesImpute improves missing value handling in proteomics data by integrating Bayesian factorization and probabilistic dropout models, outperforming existing methods in imputation and normalization.

Inferring viral proteins that act as public goods during coinfection

The paper infers viral proteins acting as public goods during coinfection in bacteriophage MS2 using experimental evolution data and Bayesian inference.

Knowledge-guided Bayesian optimization using pre-trained LLMs speeds up the identification of superior genotypes from germplasm collection

This paper uses pre-trained LLMs to enhance Bayesian optimization for selecting superior genotypes from germplasm collections, improving efficiency with passport data and genomic information.

SAS_MoCa: a software for small-angle scattering data analysis of large unilamellar vesicles

SAS_MoCa is an open-source Python package for analyzing small-angle scattering data from large unilamellar vesicles, providing posterior parameter distributions and uncertainties using a Bayesian framework.

Machine Learning Guided Optimization of an Oral Microemulsion System: A Bayesian Optimization Approach

This study uses Bayesian optimization to design stable oral microemulsions, identifying high-performing formulations with improved drug loading and stability.

Uncovering internal states with a robust shared-state multi-neuron GLM-HMM framework

This paper introduces a robust GLM-HMM framework for analyzing neural data to uncover latent internal states and their relationship to behavior.

Decoupling CAR-T Expansion, Conversion, and Decay Timing: Physiologically Aligned Semi-Mechanistic Modeling with Smooth Gating and a Cauchy Likelihood Residual Model

This paper introduces a Cauchy likelihood residual model for CAR-T cellular kinetics, showing it improves robustness and physiological plausibility compared to Gaussian and Student's t models.

Direct probabilistic quantification of mosaic loss of chromosome Y from sequencing data

This paper introduces BaySeq-Y, a Bayesian method to directly quantify mosaic loss of chromosome Y from sequencing data, showing improved accuracy over existing methods.

Host species background, defence systems, and phage tail gene architecture shape phage infectivity in cystic fibrosis-associated Achromobacter

The paper shows bacterial strain identity is the main factor determining phage infectivity in Achromobacter, with lineage and strain identity explaining 90% of variation in infection efficiency.

Indirect genomic effects shape cancer risk across species

This study identifies genes linked to cancer risk variation across species, showing indirect effects on body size dominate cancer suppression, while direct effects increase tumour prevalence.

Phylogenetic Dependence and Effective Information in Species-Level Model Evaluation

This paper develops a framework to quantify effective information in species-level analyses by accounting for phylogenetic dependence, showing that nominal species counts overestimate independent information.